Programming Patterns and Tools for Cloud Computing...• HDFS (Hadoop, Big Data) • Does not...

Programming Patterns and Tools for Cloud

Computing SeSe ACC

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AmazonWebServicesHPHelionBackspaceSNICCloud

GoogleAppEngineOpenShiftCloudFoundry……

GoogleDocsOffice365DropboxStochSS

2SamJohnson,http://creativecommons.org/licenses/by-sa/3.0/

Basicservicemodels:IaaS,PaaS,SaaS

Virtual Appliances

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Installationofcompletesoftwarestackpackagedasvirtualmachineimages.

• Distributedasfiles(imageformats)ordirectlyinacloudenvironment

• TypicallyusedtopackageupasingleapplicationasaVMI,simpledistribution

• CommonwaytomakeSaaSoutoflegacyapplication• Caneasymovebetweendifferenthostingenvironments(Public/PrivateCloud,VirtualBox,VMWareetc)

• Userstypicallydeployandusethemasvirtualprivateresources

https://en.wikipedia.org/wiki/Virtual_appliance

Platform as a Service (PaaS)

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Thecapabilityprovidedtotheconsumeristodeployontothecloudinfrastructureconsumer-createdoracquiredapplica\onscreatedusingprogramminglanguages,libraries,services,andtoolssupportedbytheprovider.Theconsumerdoesnotmanageorcontroltheunderlyingcloudinfrastructureincludingnetwork,servers,opera\ngsystems,orstorage,buthascontroloverthedeployedapplica\onsandpossiblyconfigura\onse^ngsfortheapplica\on-hos\ngenvironment.

AmajoraimofPaaSistoaidindevelopmentof“cloudy”applicationsby:

• ReducingtheburdenofexplicitlymanagingIaaS• Cloudinteroperability(agoalforsomeofthem)• Enforcingprogrammingmodels/frameworksthatleadstodesignsthatmakegooduseofthecloudmodel.

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Platform as a Service (PaaS)DirectlyconsumingIaaSgivesgreatflexibilityandcontrolbutmaybetoocomplicatedfortheaverageuser.PlatformasaServiceaimsatsimplifyingapplicationdevelopmentbyabstractingawayIaaS.Also,insomecases,byenforcingcertainenvironmentsandprogrammingmodels,theplatformcanprovideaddedvalue,particularlyautoscaling(seeJohanTordsson’slectures).

Example: Google App Engine

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• Letsyoueasilybuildscalablewebapplications• DevelopinalocalSDKsandbox,thendeploytoGoogleCloudwithnocodemodification

• Theplatformwillautoscaleyouwebservicetomeetincreasedloadandtraffic• Supportsdifferentlanguages,e.g.Python,Java,GoandyourfavoriteframeworkslikeDjango,Jinja2etc.

• ButnotallpossiblePythonpackages(sandbox)• Worksreallywellformany(traditional)webapplications• Sandboxcanbelimitingfornon-typicalapplications,likethosefromCSE

Example: CloudFoundry

http://12factor.net/

https://www.cloudfoundry.org/platform/

Example: OpenShift

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https://www.openshift.org/

FromRedHat.SimilargoalsasCloudFoundy,basedonDockerandKubernetes

Software as a Service

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Thecapabilityprovidedtotheconsumeristousetheprovider’sapplica\onsrunningonacloudinfrastructure.Theapplica\onsareaccessiblefromvariousclientdevicesthrougheitherathinclientinterface,suchasawebbrowser(e.g.,web-basedemail),oraprograminterface.Theconsumerdoesnotmanageorcontroltheunderlyingcloudinfrastructureincludingnetwork,servers,opera\ngsystems,storage,orevenindividualapplica\oncapabili\es,withthepossibleexcep\onoflimiteduser-specificapplica\onconfigura\onse^ngs.

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www.molns.org,github.com/MOLNs/molnsCloudvirtualappliance/platformforlargescalesimulationswithPyURDME

Case study: MOLNs and StochSS

The tasks: Spatial Stochastic Simulations in Systems Biology

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PyURDME(www.pyurdme.org)isamodelingandsimulationlibraryforstochasticreaction-diffusionsimulationsofbiochemicalnetworks.

• Stochasticprocesssimulatedonacomputationalmesh.

• Applicationsincludestudyinggeneregulation,yeastpolarization.

• Outputisatimeseriesofspatialdata,X.

What problems did we want to solve?

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1. PyURDMEsimulations,inparticularparametersweeps,requirelargecomputationalresources.

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Xisjustonepossibleoutcome,weneedtogeneratelargeensemblesofrealizationstoconductastatisticalanalysis.

1k-1Mtrajectories—> 10*1000~10Gbdata

Fallsinthecategory“High-TroughputComputing(HTC)”and“Many-TaskComputing(MTC)”

Potential solution, use HPC -clusters

• We did that for a while, and used them to make large sweeps and write papers ourselves. Then we started to think about the problem of scientific software. There were problems: • The modeling process really benefits from interactivity,

but HPC resources are not for that. • It was daunting to try to write software to work with any

type of university cluster. • The software stack needed is complex and fragile and

would likely be very time-consuming to install in the HPC environment (leading to long wait times for users)

• Computational experiments become hard to reproduce since they rely on specific resources and organizations.

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Components in the implementation

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Amazon EC2

HP Helion

OpenStack

PyURDME: !Spatial stochastic modeling

and simulation engine

molnsclient: automatic setup and provisioning of a distributed !! ! ! computational environment - the “virtual lab”

molnsutil: Library for scalable parallel execution of simulation engines. !! ! Unified data management in the cloud environments.

IPython project: Web based notebook front-end to Python. !! ! ! ! Sharable, computable documents. !

! ! ! ! ! ! Parallel computing in via Ipython.parallel.

MOLNs

Simulation engine

Data analysis frameworks

Simulation engine

Summary of Experiences so far

• Virtual appliance for building VM images with the software stack for one OS only reduces time to release, critical for a small science team.

• Some parts were easy, essentially just deploying existing software in a virtual environment (e.g. IPython)

• Special care was needed for data transport + Special care was needed for storage abstractions

• As the stack grows, the large monolithic virtual appliance/image is starting to give us problems. We are now looking into alternative technologies as a remedy, e.g. containers and more extensive use of orchestration tools.

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What we have not cover in this course that is important for developing SaaS

• Scalable web sites/services • frameworks • sessions • authentication • databases • security

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Inpractice,thisisanintegralpartofalmostallcloudcomputingsoftwareprojects,butheremanysourcesofinformation,toolsandframeworks(Webapp2,Djago,Flask..),PaaS(GAE…),existthatwillmakeyourlifefairlyeasy.

“I need my application to scale”• What do we mean with a scalable CSE application?

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Tightly coupled applications?• Common in CSE, typical examples are Partial Differential

Equation (PDE) solvers

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Communicationoverblock-boundaries

HPC programming models • Message Passing Interface (Distributed Memory) • OpenMP, Pthreads etc. (Shared Memory) • CUDA, OpenCL (GPGPU) • FGPA

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• Low-latencyinterprocesscommunicationiscriticaltoapplicationperformance• Specializedhardware/instructionsets.• “Everycyclecounts”,usersofHPCbecomeconcernedaboutVMoverhead• Failedtaskscausesmajorproblems/performancelosses• Computeinfrastructureorganizedaccordingly• ’High-PerformanceComputing,HPCclusterresources.Waitinaqueuetoobtaindedicateddirectaccesstohigh-bandwidthhardwareresources.

“Cloud computing doesn’t scale”

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Whenyouhearthosetypesofstatements,theytypicallyrefertoconceptionsabouttraditionalcommunicationintensiveHPCapplications,typicallyimplementedwithMPI.

• Referstolow-performingnetworkbetweeninstancesinPublicClouds• DoesnottypicallyaccountfortailoredHPCclusterofferingsandplacementgroups(availableine.g.EC2)

Representsaverynarrowviewonwhatapplicationsandprogrammingmodelsareadmissible.

http://star.mit.edu/cluster/StarClusterdeploysMPI-clustersoverAWS:

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Vertical vs. Horizontal Scaling

Verticalscaling(“scale-up”): Increasethecapacityofyourserverstomeetapplicationdemand.

• AddmorevCPUs• AddmoreRAM/storage • FundamentallylimitedbyHWcapacityoftheunderlyinghost• Costincreasessharplyforhigh-endmachines

Horizontalscaling(“scale-out”): Meetincreasedapplicationdemandbyaddingmoreservers/storage/etc.

• Alsohaslimitations,butalotofworkgoingontoimprovescale-outproperties.• Costofaddinganothermachineofthesame(commodity)typescaleslinearlyTypicallywhatwearemostinterestedininCloudComputing.

Strong and Weak Scaling

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Strongscaling:Howdoestheexecutiontimedecreaseasafunctionofincreasedresources,forafixedproblemsize?

HowquicklycanIsolveaproblemofthissizebyaddingmoreHW?

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Weakscaling:Howdoestheexecutiontimebehaveasafunctionofincreasedresources,foranincreasingproblemsize?

Strong and Weak Scaling

HowmuchcanIgrowmyproblemsizeandstillmaintainthesameexecutiontime?

Scalability and Elasticity

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Capabili\escanbeelas\callyprovisionedandreleased,insomecasesautoma\cally,toscalerapidlyoutwardandinwardcommensuratewithdemand.Totheconsumer,thecapabili\esavailableforprovisioningomenappeartobeunlimitedandcanbeappropriatedinanyquan\tyatany\me.

Designprinciplestoallowforhorizontalscalingandelasticity?

(Discuss/Brainstorminsmallgroupsfor5min)

Strive for statelessness of task/component

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Controller

Worker

“Growingiseasierthanshrinking”Data,taskinput/output

Butofcourse,allcomputations/applicationsneeddataandstate.Itisnotsomuchaboutifyoustoreitasitisaboutwhereyoustoreit.

Ideallyyouwantyourtasktobeabletoexecuteanywhere,atanytime.

• Robustness• Canincrease,decreasenumberofworkers

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Controller

Worker

Isthecontrollermachinedesignedforscalablestorage?

Highpressureoncriticalcomponent(robustness)

Networkapotentialperformancebottleneck

Use the object store when you can

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Controller

Worker

S3,Swift

key-valuestorage,designedforhorizontalscaling

Inthiskindofmaster-slavesetupthecontrollerwillstillmaintainalotofinformation,routetasksetc.

Whyevenstoretaskinformationoncontroller?Performance(latency)isonegoodreason

Use the object store when you can

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Controller

Worker

S3,Swift

Decouplethelife-timeofcomputeserversanddata!

• Neededforelasticity• Serversarecattle

http://www.slideshare.net/randybias/pets-vs-cattle-the-elastic-cloud-story

• Datacanbeprecious

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Controller

Worker

Anotheroptionforrobustness,storereplicasofdataovermultiplehostsinadistributedfilesystem.

• HDFS(Hadoop,BigData)• Doesnotdecouplecompute/storagelifetimesaseasily• DesignedforextremeI/Othroughput

AsynchronousTask

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Controller

S3,Swift

Aimtoexecutetasksinnon-blockingmode,andhaveasfewglobalsynchronizationpointsaspossible.

32http://abhishek-tiwari.com/post/amqp-rabbitmq-and-celery-a-visual-guide-for-dummies#fn:1

key,valuestoredatabaseRedis

Example: Celery/RabbitMQ (Lab3)

Celery-DistributedTaskQueue

Our favorite example: MOLNs

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PyURDME(www.pyurdme.org)isamodelingandsimulationlibraryforstochasticreaction-diffusionsimulationsofbiochemicalnetworks.

Wehaveseenhowweworkedwithautomation,orchestrationetctomakethingsintocloudservice,let’slookabitatthecomputationalproblem.

What problems did we want to solve?

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1. PyURDMEsimulations,inparticularparametersweeps,requirelargecomputationalresources.

• MonteCarlo• Tasksarenumerous• Butindependent

mostlyMTCHPC,HTCorMTC?

Butalotofthetimewespendbuildinganddebuggingmodels…

ordevelopingsolvers

Forthis,wedon’tneedbigresources,butuserinteractivityisinvaluable.

Interactive Computation

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Personalopinion:OneofthemoreexcitingthingsaboutcloudcomputingtechnologiesfromtheCSEperspectiveistheopportunitytoenableinteractiveparallelcomputing.

Input Output

Output

Input

Interactwithpartialoutputascomputationproceeds

Non-interactive,obtaincompleteoutputwhenjobisdone.

MOLNs revisited

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Amazon EC2

HP Helion

OpenStack

PyURDME: !Spatial stochastic modeling

and simulation engine

molnsclient: automatic setup and provisioning of a distributed !! ! ! computational environment - the “virtual lab”

molnsutil: Library for scalable parallel execution of simulation engines. !! ! Unified data management in the cloud environments.

IPython project: Web based notebook front-end to Python. !! ! ! ! Sharable, computable documents. !

! ! ! ! ! ! Parallel computing in via Ipython.parallel.

MOLNs

Simulation engine

Data analysis frameworks

Simulation engine

Architecture

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Workers

Controller

Workers

WebBrowser

Molns Client

SharedStorage

PersistentStorage

(S3 / Swift)

InfrastructureDeployment

IPythonNotebookServer

IPythonController

IPythonEngine(s)

IPythonEngine(s)

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IPython ParallelTwotypesofviewstoyourcluster:

• direct_view• Supportsexplicitmappingofdata/taskstoengines(workers)• Runtasksonspecificmachines• MPI

• load_balanced_view• Asynchronoustaskqueue(usesZeroMQ)

Enginesalwaysblockonexecution

AsetofschedulersprovideasynchronousinterfacetoClients

IPython Parallel

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• Let’syouworkwithmanymodelssimultaneously• InteractivelyfromIPython(Notebook)• Idealforrapidprototyping/simpleparallelflows

IPparallelinClouds:Doesnotattempttosolvetheproblemofdatamanagement

Oneofthegoalsof‘molnsutil’

Inmolnsutilwehaveabstractionsforstoragesystems: SharedStorage() PersistentStorage() LocalStorage()

Programming Patterns and Tools for Cloud

ComputingPart 2: “Big Data”

What is “Big Data”

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Noteasytodefinejustbasedonsize:• Movingtarget,today’s“big”mightbetomorrows“small”• Rightnow,maybemultipleTBstoPBs(maybe)• Dependsonthetypeofdata(structured,unstructured)• Dependsonthe“velocity”ofthedata(streamingdata)

Relativetostate-of-theartcommoditysystems: Theproblemis“BigData”ifitcan’tbestored,handled,processedusingcommoditysystemsortraditionalRDMSsolutions.Typically,singleworkstations/disksarenotsufficient.MoreaboutthisinLectures2and3.

ThethreeV’s:Volume,Velocity,Variety

The three Vs, Volume, Velocity and Variety

42http://www.datasciencecentral.com/forum/topics/the-3vs-that-define-big-data

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VolumeBigDataproblemsinvolvelargedatasets,andcomputationstypicallybecomeI/Obound:

• HardDrivescapacityto(cheaply)storelargeamountsofdatahasincreasedrapidlybutread/writespeedshavenot.

—>Distributedatasetsovermultipledisks/machinesandprocessitinparallel.

Posesfundamentalrequirementsonthesystemstosupportsuchanalysis(moreaboutthisinLectures2,3)

1000 Genomes project

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Today,DNAsequencesfrom~2600individuals—>>460TBorrawdata(fullgenomicsequences),andgrowing.

Aim:Createanatlasofthehumangeneticvariation

FindbiomedicalrelevantDNAvariations

Understandsimpleandcomplextraits,andthein-betweens.

Capturegeneticvariationsthatisfoundinatleast1%ofthehumanpopulation

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Fig. 3. Cloud computing usage in big data.

Ibrahim Abaker Targio Hashem, Ibrar Yaqoob, Nor Badrul Anuar, Salimah Mokhtar, Abdullah Gani, Samee Ullah Khan

The rise of “big data” on cloud computing: Review and open research issues

Information Systems, Volume 47, 2015, 98–115

http://dx.doi.org/10.1016/j.is.2014.07.006

FrameWorks for Big Data

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Problemswithverylargedatasetsrequirespecialframeworks:• Hadoop• Hive• Spark• …• …

Focusof“LargeDatasetsforScientificApplications” CanbefollowedasaPhDcourse.March28-endofsemester.

47Theriseof“bigdata”oncloudcomputing:Reviewandopenresearchissues

What is Hadoop?

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OriginalImplementationofMapReducebyGoogle.(Read“googlemapreduce.pdf”intheportal)

HadoopisanOpenSourceimplementationinspiredbyGoogle’soriginalframework.

Firstdevelopers:DougCuttingandMikeCafarellaatthattimeatYahoo!,namedafterCutting’sson’stoyelephant.

Hadoop

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Today,theHadoopecosystemisquitelarge,andcontainsmanyanalyticstools.Thecorecomponentsare:

• HDFS(DistributedFilesystem)• Hadoopcommons(libraries,utilitiesetc.)• YARN(Resourcemanagementplatform,notin1.2.1)• MapReduce

What is MapReduce ?

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• Aprogrammingmodelforwriting(simple)(data)parallelprograms• Aframeworktoexecutesuchprograms.

Advantages:IfyoumanagetoexpressyourproblemasMapReduce,theframeworkhandles:

• Datadistribution(HDFS)• Fault-tolerance(onmanylevels)• Bringscomputationsclosetodata*• Runsoncommodityhardware• Scalesto1000’sofnodes(failureisinevitable) • Simpleprogrammingmodel(whenyouknowit…)

Thedata(andthecluster)shouldbeverylargefortheadvantagestobecomeapparent.

ItwasdesignedforthecaseofcommodityHWwithouthighperformancenetworkconnects.YoushouldnotthinkofHDFSasa“HPCparallelfilesystem”.Youdon’tcommunicatedatatocompute,youcommunicatecomputetodata.

Basic design

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http://en.wikipedia.org/wiki/Apache_Hadoop

HandlesJobs,Tasks,logicofcomputingclosetodata

DistributedFilesystem,HDFS

Resources

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http://www.michael-noll.com/tutorials/running-hadoop-on-ubuntu-linux-single-node-cluster/

ThereareacoupleofexcellentbasictutorialsforgettingstartedtoplayingwithHadooponsinglenodeclustersorsmalldistributedclusters(yes,thisisfun,IrecommenditifyouhavethetimeandasomeLinux(admin)experience).

http://www.michael-noll.com/tutorials/running-hadoop-on-ubuntu-linux-multi-node-cluster/

Also,itisrathereasytosetupa“toy-hadoop”onyourownlaptop:

https://hadoop.apache.org/docs/r1.2.1/single_node_setup.html

Setting up Hadoop in Production

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Thisisacomplexprocess.Inpractice,therearemanyvendorsthatprovidehostedservices,orprojectstomakeitsimplere.g.

• Cloudera• Hortonworks• ElasticMapReduce(EMR)inAWS• OpenStackSahara • ApacheBigtop

—>MoreimportanttogetfamiliarwithMapReduce.

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https://developers.google.com/appengine/docs/python/dataprocessing/

So how do you use it?

55Frameworkprovidesoptimizedsort,shuffle.

Youprovide.

Key stages of MapReduce

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1.Splitinput2.Map3.Shuffle4.Reduce

Hadoop Streaming

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WhatifIdon’twanttouseJava?

TheHadoopstreamingAPIlet’syouwritethemapperandreducerasastandaloneprogram,readingfromstdinandwritingtostdout.YoumapperandreducercannowbewrittenusingPython,Ruby,Bashshell,R,younameit.

catinput.txt|./map|sort|./reduce>output.txt

Logically,itcorrespondstothefollowingUnixpipe:

Performance guidelines

•ThereisasignificantlatencyinstartingMap-tasksbytheframework•Mapsshoulddoconsiderablework(ruleofthumb,workatleast1min)•Defaultblocksizeis64-128MB,inputfilesshouldbelarge(GBsized)•Worksbestfor“one-shot”computations,notiterativeflows,duetothelargelatency.

•UseCombiner(localreduce)toreducethenetworkoverheadforshuffle/reduce.

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Criticism against MapReduce• Restricted programming model. • Performance

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http://homes.cs.washington.edu/~billhowe/mapreduce_a_major_step_backwards.html

Anoldbutstillinterestingdiscussion:

http://blog.tonybain.com/tony_bain/2010/09/was-stonebraker-right.html

• Not novel

Apache Spark

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